Genomic rearrangements and the evolution of clusters of locally adaptive loci
Bibliographic record
Abstract
Numerous studies of ecological genetics have found that alleles contributing to local adaptation sometimes cluster together, forming "genomic islands of divergence." Divergence hitchhiking theory posits that these clusters evolve by the preferential establishment of tightly linked locally adapted mutations, because such linkage reduces the rate that recombination breaks up locally favorable combinations of alleles. Here, I use calculations based on previously developed analytical models of divergence hitchhiking to show that very few clustered mutations should be expected in a single bout of adaptation, relative to the number of unlinked mutations, suggesting that divergence hitchhiking theory alone may often be insufficient to explain empirical observations. Using individual-based simulations that allow for the transposition of a single genetic locus from one position on a chromosome to another, I then show that tight clustering of the loci involved in local adaptation tends to evolve on biologically realistic time scales. These results suggest that genomic rearrangements may often be an important component of local adaptation and the evolution of genomic islands of divergence. More generally, these results suggest that genomic architecture and functional neighborhoods of genes may be actively shaped by natural selection in heterogeneous environments. Because small-scale changes in gene order are relatively common in some taxa, comparative genomic studies could be coupled with studies of adaptation to explore how commonly such rearrangements are involved in local adaptation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".